Method for detecting objects in an environment of a vehicle by determining object occlusions, computing device and sensor system

Abstract

The invention relates to a method for detecting objects in an environment of a vehicle, having the following steps: receiving sensor data describing objects in the environment from an environmental sensor of the vehicle; determining respective corner portions of the objects, wherein the corner portions describe outer boundaries of the respective objects; determining a relative orientation of the respective corner portions with respect to the environmental sensor; ordering the determined corner portions in a predetermined angular direction; checking whether each corner portion is occluded by other ones of the objects along the angular direction depending on the relative orientation of the respective corner portion with respect to the environmental sensor; determining a detectable area of the respective object with respect to the environmental sensor depending on the checking of the corner portions.

Description

Technical Field

[0001] This invention relates to a method for detecting objects in a vehicle environment. Additionally, this invention relates to a computing device and sensor system for a vehicle. Finally, this invention relates to a computer program and a computer-readable (storage) medium. Background Technology

[0002] Sensor systems for vehicles with environmental sensors are known in the prior art. These sensors provide corresponding sensor data that describes the vehicle's environment. Objects in that environment, particularly other traffic participants, can then be identified based on this sensor data. Reliable identification of other objects in the environment, especially other traffic participants, is crucial for the automated or autonomous operation of the vehicle.

[0003] To reliably detect and track other traffic participants over time, it is common practice to estimate their presence. If this presence or probability is too low, detection of traffic participants cannot be relied upon. Furthermore, there are instances where real objects are temporarily and / or partially obscured by other objects. This reduces the object's probability of presence. Additionally, the object can be removed during tracking, causing the vehicle's driver assistance systems to stop reacting to it. In the worst-case scenario, this can lead to a collision with the object.

[0004] In existing technologies, most only consider the detection area or field of view of the environmental sensor. Once an object is present in the detection area, the system anticipates measuring or detecting that object based on sensor data. Therefore, the probability of presence increases. However, if no measurement is received, the probability of presence decreases, and the object is discarded. Thus, real and relevant objects are identified as erroneous measurements due to occlusion. A so-called "ray tracing model" can also be used to individually occlude objects for each environmental sensor. This is particularly problematic in urban traffic, where there are numerous traffic participants or objects in the vehicular environment. Therefore, this approach leads to very high computational costs. Summary of the Invention

[0005] The objective of this invention is to provide a solution that can more effectively and reliably detect objects in a vehicle environment while taking occlusion into account.

[0006] According to the invention, the objective is achieved by a method, computing device, sensor system, computer program, and computer-readable (storage) medium having the features according to the independent claims. Advantageous improvements of the invention are described in the dependent claims.

[0007] The method according to the invention is used to detect objects in a vehicle environment. The method includes: receiving sensor data describing objects in the environment from an environmental sensor of the vehicle. The method further includes: determining corresponding corners of the objects, wherein the corners describe the outer boundaries of the corresponding objects. Furthermore, the method includes: determining the relative orientation of the corresponding corners relative to the environmental sensor. Furthermore, the method includes: sorting the determined corners according to a predetermined angular direction. Furthermore, the method includes: checking along the angular direction whether each corner is occluded by other objects in the environment, based on the relative orientation of the corresponding corners relative to the environmental sensor; and determining the detectable area of ​​the corresponding object for the environmental sensor based on the corner checks.

[0008] This method aims to detect objects and, in particular, other traffic participants in the vehicle environment. The method can be implemented using appropriate computing devices, such as the vehicle's sensor system or electronic control equipment. During vehicle operation, sensor data can be recorded using environmental sensors. Here, the sensor data describes the vehicle's environment or a region of the environment. Measurement cycles that are sequentially performed in time can be executed using environmental sensors, with sensor data provided in each measurement cycle. The sensor data can then be transferred from the environmental sensors to the computing device for further evaluation. In principle, the method can also be implemented based on sensor data from multiple environmental sensors.

[0009] Relevant objects in the environment can be identified based on sensor data. Furthermore, the orientation of the object relative to the environmental sensor can be determined based on the sensor data. It is also proposed to determine the corners of the object. A corner, or edge, describes the outer boundary of the object. For example, the leftmost and rightmost corners can be defined as said corners. Specifically, two corners describing the spatial extension of the object in a predetermined angular direction can be determined. The angular direction can be, in particular, an azimuth direction. Each corner of the corresponding object is associated with an angle. The corners are then sorted according to their angle values ​​in the angular direction or azimuth direction. Specifically, the corners can be sorted in ascending or descending order according to their angle values. Then, each corner is checked sequentially in the angular direction. For each corner, it can be checked whether each corner is occluded by one or more other objects or whether the corner is visible to the environmental sensor. Here, the occlusion or visibility of the corner is checked based on the determined relative orientation of the object or its corner relative to the environmental sensor.

[0010] Therefore, from examining all corners of objects in the vehicle environment, it can be deduced which areas or parts of the corresponding objects are occluded and which are visible to the environmental sensors. A detectable region can also be determined for each object. The detectable region describes the area or part of the object that can be detected or seen by the environmental sensors. In this method, a corner list is determined and these corners are examined sequentially or along the angular direction. Therefore, the following advantage is obtained: the corner list is processed only once for a measurement cycle or evaluation step. The computational cost can thus be significantly reduced compared to known tracking models. Therefore, the occlusion of objects in the environment can be determined more efficiently and reliably.

[0011] Furthermore, it is preferable to determine the portion of the object located within the detection area of ​​the environmental sensor, and to determine the detectable area based on this portion. Also known as the visible area or the detection area of ​​the field of view, this describes the area where the environmental sensor can detect objects. Objects can be tracked or traced based on measurements taken with the environmental sensor. Therefore, for example, the position and / or spatial dimensions of the object are known from previous measurement cycles. Furthermore, the detection range of the environmental sensor is known. Based on this information, it can then be determined which part of the object is located within or arranged within the detection area. These portions of the object within the detection area can then be considered when determining the detectable area. This allows it to determine the portion of the object that can currently be actually detected.

[0012] In another embodiment, an occlusion list is determined, and corresponding objects are registered in the occlusion list based on the orientation of the object's corners relative to the environmental sensor, wherein the occlusion list is updated as the corresponding corners are examined along the angular direction. As explained previously, the corners are examined sequentially or along the angular direction. The occlusion list can be updated as the corresponding corners are examined. Objects or corners can be registered in the occlusion list. Identifiers or IDs can also be associated with the identified objects. Identifiers can be registered in the occlusion list. Here, the first object in the occlusion list can be an object whose corners are visible. For example, the second object in the occlusion list can be occluded by the first object. It can also be considered whether the correspondingly examined corner describes the beginning or end of an object in the angular direction. If the corner describes the end of an object, the object can be deleted from the occlusion list.

[0013] Furthermore, it is advantageous that, in order to determine the detectable region of a corresponding object, an angle list is defined, which describes the detectable angular region of the corresponding object, and the angle list is updated when examining the corresponding corner along the angular direction. It can also be proposed to store the angle associated with the last change. Based on this angle and the previously described occlusion list, it can be determined which angular range or azimuth range of the object or part of the object is visible to the environmental sensor. Overall, the detectable region of the corresponding object can therefore be determined with minimal computational cost.

[0014] In another embodiment, the corresponding corners of the object are determined in polar coordinates. A tracking algorithm can be used to predict the corresponding position of the object at the time of measurement by the environmental sensor based on sensor data. The trajectory describing the object or its position can be converted into the detection range of the environmental sensor. The object or trajectory can then be defined in polar coordinates. Therefore, the corners can be described by angle and distance or radius, respectively. Here, the angle can specifically correspond to the azimuth angle. The relative orientation between the environmental sensor and the corner can be determined by the radius. It can also be proposed that, when calculating the relative orientation, not only the radius but also the angle between the environmental sensor and the corner is considered. In this way, occlusion can be reliably performed even at corners that are small in distance from the environmental sensor but associated with an occluded object.

[0015] Furthermore, it is advantageous to determine the probability of an object's presence based on its detectable region. Preferably, tracking or tracing of the corresponding object can also be performed based on the determined detectable region. Therefore, a response can be made to objects that are currently invisible to environmental sensors. When the object becomes visible again, there are already estimates of its position and velocity that can be used directly. This provides an advantage over known methods that remove occluded objects. Furthermore, in known methods, a certain number of measurements are required before an object is considered to be re-confirmed. Additionally, a specific initialization time is required to estimate the complete movement state.

[0016] By means of the method according to the invention, the calculation of visibility based on the detection area is combined with occlusion by other objects. In dense downtown traffic, the number of other objects in the environment in this area can be between 100 and 200. In the worst case, mutual occlusion must be calculated for all possible combinations of objects. The method according to the invention can significantly reduce computational costs. Here, experiments show that the computation time increases linearly with the number of objects in the environment.

[0017] The computing device for a vehicle sensor system according to the invention is designed to execute the design scheme according to the invention and its advantages. The computing device can be particularly incorporated into the vehicle's electronic control equipment.

[0018] The sensor system according to the invention includes a computing device according to the invention and at least one environmental sensor. The environmental sensor can be designed as a radar sensor, a lidar sensor, or a camera. The environmental sensor can also have a detection range of 360° relative to the azimuth direction. The sensor system can also have multiple environmental sensors and different types of environmental sensors.

[0019] The method according to the invention can also be used if an environmental sensor with a detection range of 360° is used. Here, an angular range, for example, from 0° to 360° or from -180° to 180°, can be defined. Problems arise with objects extending beyond the defined angular limits of 180° to -180° or 360° to 0°. Currently, for objects extending beyond the angular limits, the approach is to divide the object into two sub-objects. This division is performed at the angular limits. In other words, all four corners are then associated with this object. However, the same identifier can be associated with both sub-objects.

[0020] The sensor system can be part of a vehicle's driver assistance system. With the aid of the driver assistance system, the vehicle can be operated automatically or autonomously. The vehicle according to the invention includes the sensor system according to the invention. This vehicle is specifically designed as a passenger vehicle.

[0021] Another aspect of the invention relates to a computer program comprising instructions that, when executed by a computing device, cause the computing device to perform the method and its advantageous design according to the invention. Furthermore, the invention relates to a computer-readable (storage) medium comprising instructions that, when executed by a computing device, cause the computing device to perform the method and its advantageous design according to the invention.

[0022] The preferred embodiments and advantages proposed with reference to the method according to the invention are accordingly applicable to computing devices according to the invention, sensor systems according to the invention, driver assistance systems according to the invention, vehicles according to the invention, computer programs according to the invention, and computer-readable (storage) media according to the invention.

[0023] Other features of the invention are derived from the claims, drawings, and description of the drawings. The features and combinations thereof mentioned above in the specification, and the features and combinations thereof mentioned below in the description of the drawings and / or shown separately in the drawings, may be used not only in the combinations described separately, but also in different combinations or individually, without departing from the scope of the invention. Attached Figure Description

[0024] The invention will now be explained in more detail with reference to preferred embodiments and the accompanying drawings. Herein lies:

[0025] Figure 1 A schematic diagram showing a vehicle and objects in the vehicle's environment, the vehicle including a sensor system with environmental sensors;

[0026] Figure 2a -c shows a diagram of traffic conditions at successive points in time, with two other vehicles in the environment of that vehicle;

[0027] Figure 3 A schematic flowchart illustrating a method for detecting objects in a vehicle environment;

[0028] Figure 4 A schematic diagram of an environmental sensor and an object is shown, wherein the corners of the object are defined in polar coordinates;

[0029] Figure 5 A schematic diagram showing an environmental sensor and three objects is provided, two of which are partially obscured from the view of the environmental sensor; and

[0030] Figure 6a -d is used to determine based on Figure 5 The different evaluation steps for object occlusion show the occlusion list, angle list, and regions with angles.

[0031] Elements with the same or the same function in the accompanying drawings are given the same reference numerals. Detailed Implementation

[0032] Figure 1 A schematic top view of vehicle 1 is shown, where the vehicle is designed as a passenger bus. Vehicle 1 includes a sensor system 2, which can detect objects Ob1, Ob2, and Ob3 in the environment 5 of vehicle 1. Figure 1 The image exemplarily illustrates an object Ob1 in the environment 5 of vehicle 1. Sensor system 2 includes environmental sensors 4 and 4', which can provide measurements or sensor data describing object Ob1 in environment 5. In the current example, sensor system 2 includes a first environmental sensor 4 designed as a radar sensor and a second environmental sensor 4' designed as a camera.

[0033] Furthermore, the sensor system 2 includes a computing device 3, which can be formed, for example, by an electronic control device. Sensor data provided by environmental sensors 4, 4' is transmitted to the computing device 3 and evaluated accordingly to identify object Ob1. For this purpose, a corresponding computer program can be run on the computing device 3.

[0034] Figures 2a to 2c A schematic diagram showing vehicle 1 in traffic conditions in the city center is provided. Figures 2a to 2c Describe the successive time steps. According to Figure 2aVehicle 1 follows a first object Ob1, which is another vehicle. Vehicle 1 and the first object Ob1 move toward intersection 6. A second object Ob2, which is another vehicle, moves from the right toward intersection 6. In this traffic situation, the second object Ob2 has the right-of-way at intersection 6. Here, when the second object Ob2 is within the monitoring area of ​​the corresponding environmental sensors 4 and 4', the second object Ob2 can be fully detected by the environmental sensors 4 and 4' of vehicle 1. In this way, the second object Ob2 can be tracked using a tracking algorithm.

[0035] Figure 2b The traffic situation at a later point in time is shown. Here, the first object Ob1 turns to the right, causing the first object Ob1 to partially obscure the second object Ob2 from the environmental sensors 4 and 4' of vehicle 1. Figure 2c The traffic situation at a later time point is shown. Here, for the environmental sensors 4 and 4' of vehicle 1, the first object Ob1 completely obscures the second object Ob2. If the obscuration is disregarded, the environmental sensors 4 and 4' do not provide any new information about the second object Ob2. This causes the tracking algorithm to reduce the probability of the second object Ob2's presence, since the second object Ob2 is directly in front of vehicle 1 and within the detection range of the environmental sensors 4 and 4'. After a certain period of time, the second object Ob2 can also be removed from the tracking algorithm. The probability of presence is typically determined by assessing the detection probability of the corresponding environmental sensors 4 and 4'. For example, it can be assumed that the camera is designed to detect another vehicle at a distance of 15m with a 99% probability and a false alarm rate. If the second object Ob2 is detected again after obscuration, it will be identified as a new object after a certain period of time.

[0036] Here, we need to determine the occlusion of objects Ob1, Ob2, and Ob3 in the environment 5 of vehicle 1. For example, in Figure 2b In the scenario shown, the output should be: 50% of the second object Ob2 is occluded. Based on the specifications of the environmental sensors 4 and 4', the detection probability will be significantly reduced. For example, it can be assumed that the camera detects the partially occluded object Ob2 with a 30% probability, while the radar sensor has a 70% detection probability. If the second object Ob2 is completely occluded, as... Figure 3 As shown in c, the detection probability can be considered to be within the range of 0%, thus the probability of existence cannot be reduced. In this way, the identified object Ob2 is not deleted in the tracking algorithm. This brings the following advantages: the existence of the second object Ob2 is known, and therefore, braking can be taken, for example, by vehicle 1 or the driver assistance system of vehicle 1. Once the second object Ob2 is no longer obscured, it can be associated with the current measurement of environmental sensors 4, 4'.

[0037] Figure 3A schematic flowchart of a method for detecting objects Ob1, Ob2, and Ob3 in the environment 5 of vehicle 1 is shown. This method is interpreted, for example, for environmental sensors 4 and 4', but can be performed on all environmental sensors 4 and 4' of vehicle 1. In step S1, sensor data is provided by environmental sensors 4 and 4'. Here, measurement cycles that are sequentially performed by environmental sensors 4 and 4' are executed. The sensor data from environmental sensors 4 and 4' is transmitted to a computing device 3, and in step S2, a tracking algorithm is used to predict the corresponding position of a so-called trajectory at the measurement time point of environmental sensors 4 and 4'. The trajectory describes the corresponding object Ob1, Ob2, and Ob3 in the environment 5. In step S3, the trajectory in the detection area of ​​environmental sensors 4 and 4' is then transformed or transformed into a sensor coordinate system. Then, in step S4, it is transformed into polar coordinates. In step S5, it is then checked whether the trajectory or object Ob1, Ob2, and Ob3 is located within the detection area of ​​environmental sensors 4 and 4'. In step S6, the occlusion of the corresponding objects Ob1, Ob2, and Ob3 is determined.

[0038] In step S7 of this method, association and updates are performed based on sensor data. In step S8, the existence probabilities of objects Ob1, Ob2, and Ob3 are updated. This is based on the results of steps S5 and S6. In step S9, a list of identified trajectories or objects Ob1, Ob2, and Ob3 is created and updated in step S10. Here, the update can be performed in each measurement cycle. The relevant steps of this method are explained in more detail below.

[0039] The following is based on Figure 4 The transformation to polar coordinates according to step S3 of the method will be explained in more detail. Figure 4 A schematic diagram of the environmental sensor 4 and object Ob1 is shown. As explained earlier, the detected object Ob1 is transformed into polar coordinates after being transformed into the sensor coordinate system. Here, object Ob1 is considered to be a rectangle or a two-dimensional box. In polar coordinates, object Ob1 or the corner C of object Ob1 can be described by angle θ and radius r, respectively. R C L Here, angle θ corresponds to the azimuth angle. Therefore, it is concluded that the corresponding objects Ob1, Ob2, Ob3, or boxes do not overlap. Thus, to describe object Ob1, only the angle θ at the right edge is used. R and radius r R Limited corner C R and the angle θ at the left edge L and radius r L Limited corner C L Therefore, each object Ob1, Ob2, Ob3 with four corners can be simplified to two corners C. RC L It is stored along with its associated identifier. Additionally, the corner C at the right edge can be stored. R and / or the corner C at the left edge L The coordinates.

[0040] According to step S5, objects Ob1, Ob2, and Ob3 located in the detection areas of environmental sensors 4 and 4' are identified. Here, the two outer corners C of the corresponding objects Ob1, Ob2, and Ob3 can be checked. R C L Whether it is within the detection zone. The detection zone describes the area in the environment 5 of vehicle 1 where environmental sensors 4 and 4' can detect objects Ob1, Ob2, and Ob3. The detection zone can be determined by its maximum radius and from -θ. S Extending to θ S The angle range is used to define it. First, check: corner C. R C L One of the radius r R r L Check if the spacing between the objects is greater than the maximum radius of the detection area. If so, it can be determined that objects Ob1, Ob2, and Ob3 are not within the detection area and are not visible.

[0041] Then, the azimuth extension Δθ of objects Ob1, Ob2, and Ob3 can be determined using the following formula: Δθ = θ L -θR. Then, the new angle at the left edge can be determined using the following formula: θ L =min(θ) L θ S The new angle at the right edge can be determined using the following formula: θ R =min(θ) R , -θ S Then you can use the quotient (θ) L -θ R The portion of the object within the detection area is determined by θ / Δθ. Finally, the new angle θ at the edge can be stored. L θ R It can also be checked first based on angle: whether objects Ob1, Ob2, and Ob3 are within the detection area. For example, if the corner C of objects Ob1, Ob2, and Ob3... R C L If the objects are within the detection area, then it can be concluded that objects Ob1, Ob2, and Ob3 are within the detection area.

[0042] When determining the occlusion of objects Ob1, Ob2, and Ob3 according to step S6 of the method, the corresponding outer corners C of objects Ob1, Ob2, and Ob3 are extracted.R C L Then, sort them along the azimuth direction. Afterwards, analyze the corner C according to the order or along the azimuth direction. R C L Four different cases can be identified here: the right corner C R The right corner C is associated with and visible at the beginning of objects Ob1, Ob2, and Ob3. R The left corner C is associated with and occluded at the beginning of objects Ob1, Ob2, and Ob3. L Associated with and visible at the ends of objects Ob1, Ob2, and Ob3, or at the left corner C. L It is associated with and occluded at the ends of objects Ob1, Ob2, and Ob3.

[0043] Besides having corner C R C L In addition to the list, a further list may be provided that lists the currently occluded objects Ob1, Ob2, and Ob3. This list will be referred to as Occlusion List 7. The order in Occlusion List 7 indicates the order in which objects Ob1, Ob2, and Ob3 are arranged. The first objects Ob1, Ob2, and Ob3 in Occlusion List 7 may have a minimum distance from vehicle 1 or environmental sensors 4, 4', and the next object Ob1, Ob2, and Ob3 in the list may begin behind the environmental sensors 4, 4'. Additionally, a list may be provided that describes the corresponding visible angle or azimuth angle of the objects. This list will be referred to as Angle List 8.

[0044] When checking for occlusion, first consider the rightmost upper corner C of objects Ob1, Ob2, and Ob3. R Check corner C R Are they obstructed or is another object Ob1, Ob2, Ob3 located at a corner relative to environmental sensors 4, 4'? R Previously. Therefore, corner C... R Compare with objects Ob1, Ob2, and Ob3 in the occlusion list. In the simplest case, corner C can be considered here. R C L The radii or spacing are compared with each other. However, based on the dimensions or spatial expansion of objects Ob1, Ob2, and Ob3 in the azimuth direction, the following situation may exist: the corner C of the second object Ob2 R The distance from the environmental sensor 4' to 4' is greater than the distance from the corner C of the first object Ob1. RSmaller, where the first object Ob1 occludes the second object Ob2. For this reason, for occlusion to be determined, for example, if the newly defined outermost corner C is located at the currently visible corner and the right edge. R If there are positive or negative angles, then the scalar product of the angles is determined.

[0045] If the corner C at the far right edge R It can be seen that, then, with corner C R The associated objects Ob1, Ob2, and Ob3 are registered at the first position in occlusion list 7. For objects Ob1, Ob2, and Ob3 that were previously visible at the first position and are now at the second position, the visible azimuth angle is determined. This is based on the angle θ of the newly added objects Ob1, Ob2, and Ob3 in occlusion list 7. R The last changed angle is used. The determined visible azimuth angle is entered into angle list 8 and updated with the last changed angle. If the rightmost corner C is... R If an object is not visible, it is compared with the next object Ob1, Ob2, Ob3 in occlusion list 7 to determine if it is visible. This process is repeated until the corner is identified as visible. Then, objects Ob1, Ob2, Ob3 are ordered to their correct positions in occlusion list 7.

[0046] Next, check corner C at the far left edge. L The corner C is obscured. L It is visible or if corner C L If the occlusion point is located at the first position in the occlusion list, then update the visible azimuth angle in angle list 8. Additionally, remove corner C from the current occlusion list 7. L And update the last changed azimuth angle. If corner C L If not visible, objects Ob1, Ob2, and Ob3 are removed from the current occlusion list. As a final step, the visible area of ​​objects Ob1, Ob2, and Ob3 is determined based on the visible azimuth angle and the previously calculated portion of objects Ob1, Ob2, and Ob3 within the detection area. For example, if half of objects Ob1, Ob2, and Ob3 are within the detection area, then the portion of objects Ob1, Ob2, and Ob3 within the detection area is 50%. If 60% of the detection area is not occluded by that portion, then a total of 30% of objects Ob1, Ob2, and Ob3 are visible to environmental sensors 4 and 4'.

[0047] The method is illustrated below with an example. Here, we assume that three objects, Ob1, Ob2, and Ob3, exist in environment 5 of vehicle 1. Objects Ob1, Ob2, and Ob3... Figure 5The following is illustrated by way of example. For clarity, only the environmental sensor 4 of vehicle 1 is shown here. The third object Ob3 extends within an angular range between 0° and 50°. Simplified identification: at the corner C of the environmental sensor 4 and the third object Ob3... R C L The spacing between them is 20m. The second object, Ob2, which is locally located ahead of the third object Ob3, extends within an angle range of 20° to 40°. Simplified identification: at the corner C of environmental sensor 4 and the second object Ob2. R C L The spacing between them is 15m. The first object Ob1, which is partially ahead of the second object Ob2 and partially ahead of the third object Ob3, extends within an angle range of 10° to 30°. Simplified identification: at the corner C between the environmental sensor 4 and the first object Ob1... R C L The spacing between them is 10m.

[0048] First, along the azimuth direction, consider the corresponding corners C of objects Ob1, Ob2, and Ob3. R C L Sort the data. First, consider the rightmost corner C of the third object, Ob3. R This corner is associated with an angle of 0°. Corner C R It is registered in the first position of the current occlusion list 7. Then, consider the rightmost corner C of the first object Ob1, which is associated with an angle of 10°. R Corner C R The corner of the third object, Ob3, is identified as a new visible corner and registered at the first position in occlusion list 7. The corner of Ob3 is moved to the second position in occlusion list 7. In angle list 8 with visible azimuth angles, an angle of 0° is first associated with each object, Ob1, Ob2, and Ob3. The visible azimuth angle of the third object, Ob3, is calculated to be 10° and updated in angle list 8. Furthermore, the final azimuth angle θ... L Or the angle that last changed was updated from 0° to 10°. Therefore, Figure 6a Showing occlusion list 7, angle list 8, and the final azimuth angle θ L Area 9. In the diagram, the changes in List 7, Table 8, and Table 9 are highlighted by underlining.

[0049] Figure 6b The occlusion list 7, angle list 8, and region 9 are shown for subsequent steps. Meanwhile, the rightmost corner C of the second object Ob2, associated with an angle of 20°, is also shown. R It has been inserted in the second position in occlusion list 7. Afterwards, examine the leftmost corner C of the first object Ob1, which is associated with a 30° angle.L Then, from the angle of 30° to the final azimuth angle θ of 10°. L The difference between the two values ​​is used to calculate the 20° visible angle of the first object Ob1, and this is updated in angle list 8. Additionally, the first object Ob1 is removed from occlusion list 7. The final azimuth angle θ... L The temperature has been changed from 10° to 30°.

[0050] Figure 6c The occlusion list 7, angle list 8, and region 9 are shown for subsequent steps. Here, examine the leftmost corner C of the second object Ob2, which is associated with an angle of 40°. L Then, from the angle of 40° to the final azimuth angle θ of 30°. L The difference is used to calculate the 10° visible angle of the second object Ob2, and this is updated in angle list 8. Additionally, the second object Ob2 is removed from occlusion list 7. The final azimuth angle θ... L The angle has been changed from 30° to 40°.

[0051] Figure 6d The occlusion list 7, angle list 8, and region 9 are shown for subsequent steps. Here, examine the leftmost corner C of the third object Ob3, which is associated with an angle of 50°. L Then, from the 50° angle and the final azimuth angle θ of 40°. L The difference is used to calculate the 10° visible angle of the third object Ob3, and this is updated in angle list 8. Additionally, the third object Ob3 is removed from occlusion list 7. The final azimuth angle θ... L The angle is updated from 40° to 50°, and the method ends.

[0052] As a result of this method, we find that 20° of the first object Ob1 is visible within an angular range between 10° and 30°. Therefore, 100% of the first object Ob1 is visible. 10° of the second object Ob2 is visible within an angular range between 30° and 40°. Therefore, 50% of the second object Ob2 is visible. 20° of the third object Ob3 is visible within an angular range between 0° and 10° and between 40° and 50°. Thus, 40% of the third object Ob3 is visible.

[0053] This method can also be used to detect environmental sensors with a 360° range. Such environmental sensors, designed as radar or lidar sensors, can be mounted on the roof of vehicle 1, for example. Here, the angle can be defined, for example, from 0° to 360° or from -180° to 180°. A problem may arise with objects Ob1, Ob2, and Ob3 that extend beyond the defined angular boundaries of 180° to -180° or from 360° to 0°. Errors may occur if the angles are ordered along the angular direction as previously described. Therefore, for objects Ob1, Ob2, and Ob3 that extend beyond these angular boundaries, they are divided into two sub-objects. This division is performed at the angular boundaries. Thus, a total of four outer corners are associated with one real object Ob1, Ob2, and Ob3, but the same identifier is associated with one real object Ob1, Ob2, and Ob3.

Claims

1. A method for detecting objects (Ob1, Ob2, Ob3) in the environment (5) of a vehicle (1), the method comprising the following steps: - Receive sensor data describing objects (Ob1, Ob2, Ob3) in the environment (5) from the environmental sensors (4, 4') of the vehicle (1). - Determine the corresponding corners (C) of the objects (Ob1, Ob2, Ob3). R C L ), of which the corner (C R C L Describe the outer boundaries of the corresponding objects (Ob1, Ob2, Ob3). - Determine the corresponding corner (C) R C L The relative orientation of the environmental sensor (4, 4') with respect to the environmental sensor. - The determined corner (C) is oriented at a predetermined angle. R C L Sort them. - According to the corresponding corner (C) R C L Each corner (C) is inspected along the angular direction relative to the relative orientation of the environmental sensor (4, 4'). R C L Whether it is occluded by other objects in the objects (Ob1, Ob2, Ob3), and - According to the corner (C) R C L The inspection determines the detectable area of ​​the corresponding object (Ob1, Ob2, Ob3) for the environmental sensor (4, 4').

2. The method according to claim 1, Its features are, It is also determined that the corresponding objects (Ob1, Ob2, Ob3) are located in the detection area of ​​the environmental sensor (4, 4'), and the detectable area is determined based on the portion.

3. The method according to claim 1 or 2, Its features are, Determine the occlusion list (7), wherein the corresponding objects (Ob1, Ob2, Ob3) are occluded according to the corners (C) of the objects. R C L The relative orientation of the 4' with respect to the environmental sensor (4, 4') is registered in the occlusion list, wherein the corresponding corner (C) is checked along the angular direction. R C L Update the occlusion list (7) when ).

4. The method according to claim 1 or 2, Its features are, To determine the detectable regions of the corresponding objects (Ob1, Ob2, Ob3), an angle list (8) is determined, which describes the detectable angular regions of the corresponding objects (Ob1, Ob2, Ob3), wherein the corresponding corners (C) are examined along the angular direction. R C L Update the list of angles (8) when ).

5. The method according to claim 1 or 2, Its features are, Determine the corresponding corner (C) of the object (Ob1, Ob2, Ob3) in polar coordinates. R C L ).

6. The method according to claim 1 or 2, Its features are, Based on the detectable area of ​​the object (Ob1, Ob2, Ob3), determine the probability of the existence of the corresponding object (Ob1, Ob2, Ob3).

7. A computing device (3) for a sensor system (2) for a vehicle (1), wherein the computing device (3) is designed to perform the method according to any one of claims 1 to 6.

8. A sensor system (2) for a vehicle (1), comprising a computing device (3) according to claim 7 and at least one environmental sensor (4, 4').

9. A computer program product comprising instructions that, when executed by a computing device (3), cause the computing device to perform the method according to any one of claims 1 to 6.

10. A computer-readable storage medium comprising instructions that, when executed by a computing device (3), cause the computing device to perform the method according to any one of claims 1 to 6.

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